
PL-DNB [1]} is a discriminative dataless method, built on naive Bayes. In the experiments, we use its code supplied by its authors. We run PL-DNB with different parameter configurations and leave the best scores for comparison.

STM [2]} is a topic modeling based dataless method using seed words of categories. We train this model using the public codehttps://github.com/ly233/Seed-Guided-Topic-Model implemented by its authors, and set its parameters following the suggestions in its original paper.

LapSWTM [3]} is the ancestor method of our Wsptm, where the main difference between them is the construction ways of the supervised Dirichlet prior for category topics. In the experiments, we use its code supplied by its authors and set its parameters following the suggestions in its original paper.

WeSTClass [4]} is a deep learning dataless method. We train this model using the public codehttps://github.com/yumeng5/WeSTClass implemented by its authors, and set its parameters following the suggestions in its original paper.

Support vector machines (SVMs) is a traditional discriminative supervised method. Here, we represent documents as TF-IDF features and train a SVMs classifier using the sklearn toolhttp://scikit-learn.org/stable/ with default parameter settings.

Maximum entropy discrimination latent Dirichlet allocation (MedLDA) [5]} is a traditional supervised topic model for classification. We train this model using the public codehttp://ml.cs.tsinghua.edu.cn/\(\sim \) jun/gibbs-medlda.shtml implemented by its authors. Specially, the option of inner cross-validation optimization is applied.

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